{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fb6aa99c",
   "metadata": {},
   "source": [
    "# 基于昇思MindSpore+Orangepi AIpro的CycleGAN图像风格迁移\n",
    "\n",
    "功能：使用基于昇思MindSpore框架开发的CycleGAN模型对输入图片进行风格迁移(apple2orange)。  \n",
    "样例输入：原始图片jpg文件。  \n",
    "样例输出：风格迁移后的图象。  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1451fc2e",
   "metadata": {},
   "source": [
    "# 前期准备\n",
    "\n",
    "* 基础镜像的样例目录中已包含转换后的om模型以及测试图片，如果直接运行，可跳过此步骤。如果需要重新转换模型，可以参考下面的步骤。\n",
    "* **建议在Linux服务器或者虚拟机转换该模型。**\n",
    "* 为了能进一步优化模型推理性能，我们需要将其转换为om模型进行使用；**转换指导详见全流程实验指导。**\n",
    "  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82653e5f",
   "metadata": {},
   "source": [
    "# 模型推理实现\n",
    "\n",
    "* 注意：本案例在离线推理的过程中可能会出现内存不足的问题，可以根据情况查看FAQ文档中的解决方案。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f95a2799-c2f5-405f-a4b1-722248655aa8",
   "metadata": {},
   "source": [
    "### 1. 导入三方库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2a9e0abe-f4f5-4a29-8af8-4e082955d206",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import mindspore.dataset as ds\n",
    "from mindspore import Tensor\n",
    "import mindspore.dataset.vision as vision\n",
    "\n",
    "import acl\n",
    "import acllite_utils as utils\n",
    "import constants as const\n",
    "from acllite_model import AclLiteModel\n",
    "from acllite_resource import resource_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30643cc9-9015-433b-8c31-66ed14cabdfe",
   "metadata": {},
   "source": [
    "### 2. 定义acllite资源初始化与去初始化类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b096cb19-e3e6-49fd-a38e-f55750f41154",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AclLiteResource:\n",
    "    \"\"\"\n",
    "    AclLiteResource\n",
    "    \"\"\"\n",
    "    def __init__(self, device_id=0):\n",
    "        self.device_id = device_id\n",
    "        self.context = None\n",
    "        self.stream = None\n",
    "        self.run_mode = None\n",
    "        \n",
    "    def init(self):\n",
    "        \"\"\"\n",
    "        init resource\n",
    "        \"\"\"\n",
    "        print(\"init resource stage:\")\n",
    "        ret = acl.init()\n",
    "\n",
    "        ret = acl.rt.set_device(self.device_id)\n",
    "        utils.check_ret(\"acl.rt.set_device\", ret)\n",
    "\n",
    "        self.context, ret = acl.rt.create_context(self.device_id)\n",
    "        utils.check_ret(\"acl.rt.create_context\", ret)\n",
    "\n",
    "        self.stream, ret = acl.rt.create_stream()\n",
    "        utils.check_ret(\"acl.rt.create_stream\", ret)\n",
    "\n",
    "        self.run_mode, ret = acl.rt.get_run_mode()\n",
    "        utils.check_ret(\"acl.rt.get_run_mode\", ret)\n",
    "\n",
    "        print(\"Init resource success\")\n",
    "\n",
    "    def __del__(self):\n",
    "        print(\"acl resource release all resource\")\n",
    "        resource_list.destroy()\n",
    "        if self.stream:\n",
    "            print(\"acl resource release stream\")\n",
    "            acl.rt.destroy_stream(self.stream)\n",
    "\n",
    "        if self.context:\n",
    "            print(\"acl resource release context\")\n",
    "            acl.rt.destroy_context(self.context)\n",
    "\n",
    "        print(\"Reset acl device \", self.device_id)\n",
    "        acl.rt.reset_device(self.device_id)\n",
    "        print(\"Release acl resource success\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23ab3af4-e881-432e-a3f2-9461666f6dbc",
   "metadata": {},
   "source": [
    "### 3. 定义风格迁移类，包含前处理、推理、后处理等操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7804ee5e-ab20-46f1-b188-e90c8d497afa",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CycleGan(object):\n",
    "    \"\"\"\n",
    "    class for Cartoonization\n",
    "    \"\"\"\n",
    "    def __init__(self, model_path):\n",
    "        self._model_path = model_path\n",
    "\n",
    "    def init(self):\n",
    "        \"\"\"\n",
    "        Initialize\n",
    "        \"\"\"\n",
    "\n",
    "        # Load model\n",
    "        self._model = AclLiteModel(self._model_path)\n",
    "\n",
    "        return const.SUCCESS\n",
    " \n",
    "    @utils.display_time\n",
    "    def pre_process(self, dir_path):\n",
    "        \"\"\"\n",
    "        image preprocess\n",
    "        \"\"\"\n",
    "        def read_img():\n",
    "            for dir in os.listdir(dir_path):\n",
    "                path = os.path.join(dir_path, dir)\n",
    "                img = Image.open(path).convert('RGB')\n",
    "                yield img, dir\n",
    "\n",
    "        dataset = ds.GeneratorDataset(read_img, column_names=[\"image\", \"image_name\"])\n",
    "        trans = [vision.Resize((256, 256)), vision.Normalize(mean=[0.5 * 255] * 3, std=[0.5 * 255] * 3), vision.HWC2CHW()]\n",
    "        dataset = dataset.map(operations=trans, input_columns=[\"image\"])\n",
    "        dataset = dataset.batch(1)\n",
    "\n",
    "        return dataset\n",
    "\n",
    "    @utils.display_time\n",
    "    def inference(self, dataset):\n",
    "        \"\"\"\n",
    "        model inference\n",
    "        \"\"\"       \n",
    "        for i, data in enumerate(dataset.create_dict_iterator()):       \n",
    "            img = data[\"image\"]\n",
    "            fake = self._model.execute([img.asnumpy(), ])\n",
    "        return img, fake\n",
    "            \n",
    "    @utils.display_time\n",
    "    def post_process(self, img, fake):\n",
    "        \"\"\"\n",
    "        post process\n",
    "        \"\"\"\n",
    "        fig = plt.figure(figsize=(8, 4), dpi=100)   \n",
    "        fake = Tensor.from_numpy(fake[0])\n",
    "        fake = (fake[0] * 0.5 * 255 + 0.5 * 255).astype(np.uint8).transpose((1, 2, 0))\n",
    "        img = (img[0] * 0.5 * 255 + 0.5 * 255).astype(np.uint8).transpose((1, 2, 0))\n",
    "\n",
    "        fig.add_subplot(2, 1, 1)\n",
    "        plt.axis(\"off\")\n",
    "        plt.imshow(img.asnumpy())\n",
    "\n",
    "        fig.add_subplot(2, 1, 2)\n",
    "        plt.axis(\"off\")\n",
    "        plt.imshow(fake.asnumpy())\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fafe334c-93d9-4469-b7e3-55e588018f82",
   "metadata": {},
   "source": [
    "### 4. 构造主函数，串联整个代码逻辑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d92ecab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from download import download\n",
    "\n",
    "# 获取模型om文件\n",
    "model_url = \"https://modelers.cn/coderepo/web/v1/file/MindSpore-Lab/cluoud_obs/main/media/examples/mindspore-courses/orange-pi-mindspore/05-CycleGAN/CycleGan.zip\"\n",
    "download(model_url, \"./\", kind=\"zip\", replace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e3a384d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def main():\n",
    "    MODEL_PATH =\"CycleGan.om\"\n",
    "\n",
    "    acl_resource = AclLiteResource()  # 初始化acl资源\n",
    "    acl_resource.init()\n",
    "    \n",
    "    # instantiation CycleGan object\n",
    "    cyclegan = CycleGan(MODEL_PATH)  # 构造模型对象\n",
    "    \n",
    "    # init\n",
    "    ret = cyclegan.init()  # 初始化模型类变量\n",
    "    utils.check_ret(\"CycleGan.init \", ret)  \n",
    "    \n",
    "    dir_path = \"./data\"\n",
    "\n",
    "    # preprocess\n",
    "    dataset = cyclegan.pre_process(dir_path)  # 前处理\n",
    "    # inference\n",
    "    img, fake = cyclegan.inference(dataset)  # 推理\n",
    "    # postprocess\n",
    "    cyclegan.post_process(img, fake)   # 后处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd648da3-8d25-47d8-ace2-24fdd674dfa5",
   "metadata": {},
   "source": [
    "### 5. 运行\n",
    "运行完成后，会显示原图片和推理后的图片，如下所示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "396dab1a-ced7-4b68-841c-810d0b3b65fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "init resource stage:\n",
      "Init resource success\n",
      "Init model resource start...\n",
      "[AclLiteModel] create model output dataset:\n",
      "malloc output 0, size 786432\n",
      "Create model output dataset success\n",
      "Init model resource success\n",
      "in pre_process, use time:0.005691051483154297\n",
      "in inference, use time:0.10941195487976074\n",
      "|\r"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in post_process, use time:301.1996519565582\n",
      "acl resource release all resource\n",
      "AclLiteModel release source success\n",
      "acl resource release stream\n",
      "acl resource release context\n",
      "Reset acl device  0\n",
      "Release acl resource success\n"
     ]
    }
   ],
   "source": [
    "main()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "565c3b3f-abba-4c71-b74c-d3403b2427af",
   "metadata": {},
   "source": [
    "其中，除了acl相关资源初始化和释放的信息外，“in pre_process, use time”表示前处理耗时，“in inference, use time”表示推理耗时，“in post_process, use time”表示后处理耗时，单位都为秒。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a29d51d-0e2b-4875-808a-a2deb5c91d8d",
   "metadata": {},
   "source": [
    "# 样例总结"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2b4e3a-dd3d-4db0-bc76-55f4502ea8d9",
   "metadata": {},
   "source": [
    "我们来回顾一下以上代码，可以包括以下几个步骤：\n",
    "1. 初始化acl资源：在调用acl相关资源时，必须先初始化AscendCL，否则可能会导致后续系统内部资源初始化出错。  \n",
    "2. 对图片进行前处理：使得模型正常推理。  \n",
    "3. 推理：利用AclLiteModel.execute接口对图片进行推理。  \n",
    "4. 对推理结果进行后处理：使得图片正常画出。  \n",
    "5. 可视化图片：利用plt将结果画出。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  },
  "vscode": {
   "interpreter": {
    "hash": "5d4380103ee16a53c08c2072b2cf7372aea12e3655e7cb36be2874465cdcb878"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
